CyberStrikeAI: The system of action for AI-native cybersecurity—where intent becomes governed execution, evidence becomes

by AIPentest · MCP Server · ★ 8.3k

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install CyberStrikeAI

git clone https://github.com/Ed1s0nZ/CyberStrikeAI.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 64/100

Flagged: sudo usage. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

aiai-agentsai-cybersecurityai-hackingai-penetration-testingai-security-toolctf-toolsmcppentesting-tools

Quick Facts

Stars8,311
Forks1,793
LanguageGo
CategoryMCP Server
LicenseApache-2.0
Quality Score64/100
Open Issues42
Last Updated2026-10-31
Created2025-11-08
Platformsgo, mcp
Est. Tokens~27k

About CyberStrikeAI

CyberStrikeAI 中文 | English The system of action for AI-native cybersecurity—where intent becomes governed execution, evidence becomes operational memory, and every operation improves the next. CyberStrikeAI connects planning, execution, human oversight, evidence, and replay in one auditable workspace.

Compatible Skills

These tools work well together with CyberStrikeAI for enhanced workflows:

  • Pentest-Swarm-AI — semantic(0.30)+complementary+same_lang+similar_pop+shared_platform (61%)
  • xalgorix — semantic(0.22)+complementary+same_lang+similar_pop+shared_platform (58%)
  • god-eye — semantic(0.22)+complementary+same_lang+similar_pop+shared_platform (58%)
  • hexstrike-ai — semantic(0.45)+rare_topics+similar_pop+shared_platform (56%)
  • leash — semantic(0.15)+complementary+same_lang+similar_pop+shared_platform (55%)

CyberStrikeAI alternative? Top 6 similar tools

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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